Overview

Dataset statistics

Number of variables35
Number of observations2940
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory804.0 KiB
Average record size in memory280.0 B

Variable types

Numeric15
Categorical18
Boolean2

Alerts

EmployeeCount has constant value "1" Constant
Over18 has constant value "True" Constant
StandardHours has constant value "80" Constant
Age is highly correlated with JobLevel and 3 other fieldsHigh correlation
JobLevel is highly correlated with Age and 6 other fieldsHigh correlation
MonthlyIncome is highly correlated with Age and 4 other fieldsHigh correlation
PercentSalaryHike is highly correlated with PerformanceRatingHigh correlation
PerformanceRating is highly correlated with PercentSalaryHikeHigh correlation
TotalWorkingYears is highly correlated with Age and 7 other fieldsHigh correlation
YearsAtCompany is highly correlated with Age and 6 other fieldsHigh correlation
YearsInCurrentRole is highly correlated with JobLevel and 4 other fieldsHigh correlation
YearsSinceLastPromotion is highly correlated with TotalWorkingYears and 3 other fieldsHigh correlation
YearsWithCurrManager is highly correlated with JobLevel and 4 other fieldsHigh correlation
MaritalStatus is highly correlated with StockOptionLevelHigh correlation
EmployeeCount is highly correlated with MaritalStatus and 18 other fieldsHigh correlation
BusinessTravel is highly correlated with EmployeeCount and 2 other fieldsHigh correlation
WorkLifeBalance is highly correlated with EmployeeCount and 2 other fieldsHigh correlation
EducationField is highly correlated with Department and 1 other fieldsHigh correlation
Department is highly correlated with EducationField and 1 other fieldsHigh correlation
JobInvolvement is highly correlated with EmployeeCount and 2 other fieldsHigh correlation
StockOptionLevel is highly correlated with MaritalStatusHigh correlation
EnvironmentSatisfaction is highly correlated with EmployeeCount and 2 other fieldsHigh correlation
JobSatisfaction is highly correlated with EmployeeCount and 2 other fieldsHigh correlation
RelationshipSatisfaction is highly correlated with EmployeeCount and 2 other fieldsHigh correlation
OverTime is highly correlated with EmployeeCount and 2 other fieldsHigh correlation
Over18 is highly correlated with MaritalStatus and 18 other fieldsHigh correlation
JobRole is highly correlated with Department and 4 other fieldsHigh correlation
Gender is highly correlated with EmployeeCount and 2 other fieldsHigh correlation
Education is highly correlated with EmployeeCount and 2 other fieldsHigh correlation
StandardHours is highly correlated with MaritalStatus and 18 other fieldsHigh correlation
Attrition is highly correlated with EmployeeCount and 2 other fieldsHigh correlation
EmployeeNumber is uniformly distributed Uniform
EmployeeNumber has unique values Unique
NumCompaniesWorked has 394 (13.4%) zeros Zeros
TrainingTimesLastYear has 108 (3.7%) zeros Zeros
YearsAtCompany has 88 (3.0%) zeros Zeros
YearsInCurrentRole has 488 (16.6%) zeros Zeros
YearsSinceLastPromotion has 1162 (39.5%) zeros Zeros
YearsWithCurrManager has 526 (17.9%) zeros Zeros

Reproduction

Analysis started2022-12-19 14:05:28.391528
Analysis finished2022-12-19 14:06:10.671814
Duration42.28 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

Age
Real number (ℝ≥0)

HIGH CORRELATION

Distinct43
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.92380952
Minimum18
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2022-12-19T19:36:10.798273image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile24
Q130
median36
Q343
95-th percentile54
Maximum60
Range42
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.133819193
Coefficient of variation (CV)0.2473693617
Kurtosis-0.4054998352
Mean36.92380952
Median Absolute Deviation (MAD)6
Skewness0.4130752441
Sum108556
Variance83.42665306
MonotonicityNot monotonic
2022-12-19T19:36:10.939006image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
35156
 
5.3%
34154
 
5.2%
36138
 
4.7%
31138
 
4.7%
29136
 
4.6%
32122
 
4.1%
30120
 
4.1%
33116
 
3.9%
38116
 
3.9%
40114
 
3.9%
Other values (33)1630
55.4%
ValueCountFrequency (%)
1816
 
0.5%
1918
 
0.6%
2022
 
0.7%
2126
 
0.9%
2232
 
1.1%
2328
 
1.0%
2452
1.8%
2552
1.8%
2678
2.7%
2796
3.3%
ValueCountFrequency (%)
6010
 
0.3%
5920
0.7%
5828
1.0%
578
 
0.3%
5628
1.0%
5544
1.5%
5436
1.2%
5338
1.3%
5236
1.2%
5138
1.3%

Attrition
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
0
2466 
1
474 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2940
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02466
83.9%
1474
 
16.1%

Length

2022-12-19T19:36:11.079676image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-19T19:36:11.205199image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
02466
83.9%
1474
 
16.1%

Most occurring characters

ValueCountFrequency (%)
02466
83.9%
1474
 
16.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2940
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02466
83.9%
1474
 
16.1%

Most occurring scripts

ValueCountFrequency (%)
Common2940
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02466
83.9%
1474
 
16.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2940
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02466
83.9%
1474
 
16.1%

BusinessTravel
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
Travel_Rarely
2086 
Travel_Frequently
554 
Non-Travel
300 

Length

Max length17
Median length13
Mean length13.44761905
Min length10

Characters and Unicode

Total characters39536
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTravel_Rarely
2nd rowTravel_Frequently
3rd rowTravel_Rarely
4th rowTravel_Frequently
5th rowTravel_Rarely

Common Values

ValueCountFrequency (%)
Travel_Rarely2086
71.0%
Travel_Frequently554
 
18.8%
Non-Travel300
 
10.2%

Length

2022-12-19T19:36:11.330265image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-19T19:36:11.462660image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
travel_rarely2086
71.0%
travel_frequently554
 
18.8%
non-travel300
 
10.2%

Most occurring characters

ValueCountFrequency (%)
e6134
15.5%
r5580
14.1%
l5580
14.1%
a5026
12.7%
T2940
7.4%
v2940
7.4%
y2640
6.7%
_2640
6.7%
R2086
 
5.3%
n854
 
2.2%
Other values (7)3116
7.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter30716
77.7%
Uppercase Letter5880
 
14.9%
Connector Punctuation2640
 
6.7%
Dash Punctuation300
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e6134
20.0%
r5580
18.2%
l5580
18.2%
a5026
16.4%
v2940
9.6%
y2640
8.6%
n854
 
2.8%
q554
 
1.8%
u554
 
1.8%
t554
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
T2940
50.0%
R2086
35.5%
F554
 
9.4%
N300
 
5.1%
Connector Punctuation
ValueCountFrequency (%)
_2640
100.0%
Dash Punctuation
ValueCountFrequency (%)
-300
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin36596
92.6%
Common2940
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e6134
16.8%
r5580
15.2%
l5580
15.2%
a5026
13.7%
T2940
8.0%
v2940
8.0%
y2640
7.2%
R2086
 
5.7%
n854
 
2.3%
F554
 
1.5%
Other values (5)2262
 
6.2%
Common
ValueCountFrequency (%)
_2640
89.8%
-300
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII39536
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e6134
15.5%
r5580
14.1%
l5580
14.1%
a5026
12.7%
T2940
7.4%
v2940
7.4%
y2640
6.7%
_2640
6.7%
R2086
 
5.3%
n854
 
2.2%
Other values (7)3116
7.9%

DailyRate
Real number (ℝ≥0)

Distinct886
Distinct (%)30.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean802.4857143
Minimum102
Maximum1499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2022-12-19T19:36:11.603549image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum102
5-th percentile164
Q1465
median802
Q31157
95-th percentile1425
Maximum1499
Range1397
Interquartile range (IQR)692

Descriptive statistics

Standard deviation403.4404468
Coefficient of variation (CV)0.5027384782
Kurtosis-1.203817139
Mean802.4857143
Median Absolute Deviation (MAD)344
Skewness-0.003516771484
Sum2359308
Variance162764.1941
MonotonicityNot monotonic
2022-12-19T19:36:11.762463image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69112
 
0.4%
40810
 
0.3%
53010
 
0.3%
132910
 
0.3%
108210
 
0.3%
32910
 
0.3%
8298
 
0.3%
14698
 
0.3%
2678
 
0.3%
2178
 
0.3%
Other values (876)2846
96.8%
ValueCountFrequency (%)
1022
 
0.1%
1032
 
0.1%
1042
 
0.1%
1052
 
0.1%
1062
 
0.1%
1072
 
0.1%
1092
 
0.1%
1116
0.2%
1152
 
0.1%
1164
0.1%
ValueCountFrequency (%)
14992
 
0.1%
14982
 
0.1%
14964
0.1%
14956
0.2%
14922
 
0.1%
14908
0.3%
14882
 
0.1%
14856
0.2%
14822
 
0.1%
14804
0.1%

Department
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
Research & Development
1922 
Sales
892 
Human Resources
 
126

Length

Max length22
Median length22
Mean length16.54217687
Min length5

Characters and Unicode

Total characters48634
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSales
2nd rowResearch & Development
3rd rowResearch & Development
4th rowResearch & Development
5th rowResearch & Development

Common Values

ValueCountFrequency (%)
Research & Development1922
65.4%
Sales892
30.3%
Human Resources126
 
4.3%

Length

2022-12-19T19:36:11.918980image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-19T19:36:12.059446image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
research1922
27.8%
1922
27.8%
development1922
27.8%
sales892
12.9%
human126
 
1.8%
resources126
 
1.8%

Most occurring characters

ValueCountFrequency (%)
e10754
22.1%
3970
 
8.2%
s3066
 
6.3%
a2940
 
6.0%
l2814
 
5.8%
R2048
 
4.2%
r2048
 
4.2%
c2048
 
4.2%
n2048
 
4.2%
m2048
 
4.2%
Other values (10)14850
30.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter37754
77.6%
Uppercase Letter4988
 
10.3%
Space Separator3970
 
8.2%
Other Punctuation1922
 
4.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e10754
28.5%
s3066
 
8.1%
a2940
 
7.8%
l2814
 
7.5%
r2048
 
5.4%
c2048
 
5.4%
n2048
 
5.4%
m2048
 
5.4%
o2048
 
5.4%
p1922
 
5.1%
Other values (4)6018
15.9%
Uppercase Letter
ValueCountFrequency (%)
R2048
41.1%
D1922
38.5%
S892
17.9%
H126
 
2.5%
Space Separator
ValueCountFrequency (%)
3970
100.0%
Other Punctuation
ValueCountFrequency (%)
&1922
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin42742
87.9%
Common5892
 
12.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e10754
25.2%
s3066
 
7.2%
a2940
 
6.9%
l2814
 
6.6%
R2048
 
4.8%
r2048
 
4.8%
c2048
 
4.8%
n2048
 
4.8%
m2048
 
4.8%
o2048
 
4.8%
Other values (8)10880
25.5%
Common
ValueCountFrequency (%)
3970
67.4%
&1922
32.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII48634
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e10754
22.1%
3970
 
8.2%
s3066
 
6.3%
a2940
 
6.0%
l2814
 
5.8%
R2048
 
4.2%
r2048
 
4.2%
c2048
 
4.2%
n2048
 
4.2%
m2048
 
4.2%
Other values (10)14850
30.5%

DistanceFromHome
Real number (ℝ≥0)

Distinct29
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.192517007
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2022-12-19T19:36:12.168863image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q314
95-th percentile26
Maximum29
Range28
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.105485131
Coefficient of variation (CV)0.881748179
Kurtosis-0.2264931379
Mean9.192517007
Median Absolute Deviation (MAD)5
Skewness0.9576287023
Sum27026
Variance65.69888921
MonotonicityNot monotonic
2022-12-19T19:36:12.340767image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2422
14.4%
1416
14.1%
10172
 
5.9%
9170
 
5.8%
3168
 
5.7%
7168
 
5.7%
8160
 
5.4%
5130
 
4.4%
4128
 
4.4%
6118
 
4.0%
Other values (19)888
30.2%
ValueCountFrequency (%)
1416
14.1%
2422
14.4%
3168
 
5.7%
4128
 
4.4%
5130
 
4.4%
6118
 
4.0%
7168
 
5.7%
8160
 
5.4%
9170
5.8%
10172
5.9%
ValueCountFrequency (%)
2954
1.8%
2846
1.6%
2724
0.8%
2650
1.7%
2550
1.7%
2456
1.9%
2354
1.8%
2238
1.3%
2136
1.2%
2050
1.7%

Education
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
3
1144 
4
796 
2
564 
1
340 
5
 
96

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2940
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row2
4th row4
5th row1

Common Values

ValueCountFrequency (%)
31144
38.9%
4796
27.1%
2564
19.2%
1340
 
11.6%
596
 
3.3%

Length

2022-12-19T19:36:12.493166image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-19T19:36:12.633834image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
31144
38.9%
4796
27.1%
2564
19.2%
1340
 
11.6%
596
 
3.3%

Most occurring characters

ValueCountFrequency (%)
31144
38.9%
4796
27.1%
2564
19.2%
1340
 
11.6%
596
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2940
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
31144
38.9%
4796
27.1%
2564
19.2%
1340
 
11.6%
596
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Common2940
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
31144
38.9%
4796
27.1%
2564
19.2%
1340
 
11.6%
596
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2940
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
31144
38.9%
4796
27.1%
2564
19.2%
1340
 
11.6%
596
 
3.3%

EducationField
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
Life Sciences
1212 
Medical
928 
Marketing
318 
Technical Degree
264 
Other
164 

Length

Max length16
Median length15
Mean length10.53333333
Min length5

Characters and Unicode

Total characters30968
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLife Sciences
2nd rowLife Sciences
3rd rowOther
4th rowLife Sciences
5th rowMedical

Common Values

ValueCountFrequency (%)
Life Sciences1212
41.2%
Medical928
31.6%
Marketing318
 
10.8%
Technical Degree264
 
9.0%
Other164
 
5.6%
Human Resources54
 
1.8%

Length

2022-12-19T19:36:12.759094image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-19T19:36:12.899565image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
life1212
27.1%
sciences1212
27.1%
medical928
20.8%
marketing318
 
7.1%
technical264
 
5.9%
degree264
 
5.9%
other164
 
3.7%
human54
 
1.2%
resources54
 
1.2%

Most occurring characters

ValueCountFrequency (%)
e6210
20.1%
i3934
12.7%
c3934
12.7%
n1848
 
6.0%
a1564
 
5.1%
1530
 
4.9%
s1320
 
4.3%
M1246
 
4.0%
L1212
 
3.9%
f1212
 
3.9%
Other values (16)6958
22.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter24968
80.6%
Uppercase Letter4470
 
14.4%
Space Separator1530
 
4.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e6210
24.9%
i3934
15.8%
c3934
15.8%
n1848
 
7.4%
a1564
 
6.3%
s1320
 
5.3%
f1212
 
4.9%
l1192
 
4.8%
d928
 
3.7%
r800
 
3.2%
Other values (7)2026
 
8.1%
Uppercase Letter
ValueCountFrequency (%)
M1246
27.9%
L1212
27.1%
S1212
27.1%
T264
 
5.9%
D264
 
5.9%
O164
 
3.7%
H54
 
1.2%
R54
 
1.2%
Space Separator
ValueCountFrequency (%)
1530
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin29438
95.1%
Common1530
 
4.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e6210
21.1%
i3934
13.4%
c3934
13.4%
n1848
 
6.3%
a1564
 
5.3%
s1320
 
4.5%
M1246
 
4.2%
L1212
 
4.1%
f1212
 
4.1%
S1212
 
4.1%
Other values (15)5746
19.5%
Common
ValueCountFrequency (%)
1530
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII30968
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e6210
20.1%
i3934
12.7%
c3934
12.7%
n1848
 
6.0%
a1564
 
5.1%
1530
 
4.9%
s1320
 
4.3%
M1246
 
4.0%
L1212
 
3.9%
f1212
 
3.9%
Other values (16)6958
22.5%

EmployeeCount
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
1
2940 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2940
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
12940
100.0%

Length

2022-12-19T19:36:13.025068image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-19T19:36:13.134474image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
12940
100.0%

Most occurring characters

ValueCountFrequency (%)
12940
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2940
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
12940
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2940
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
12940
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2940
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12940
100.0%

EmployeeNumber
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct2940
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1470.5
Minimum1
Maximum2940
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2022-12-19T19:36:13.259513image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile147.95
Q1735.75
median1470.5
Q32205.25
95-th percentile2793.05
Maximum2940
Range2939
Interquartile range (IQR)1469.5

Descriptive statistics

Standard deviation848.849221
Coefficient of variation (CV)0.5772521054
Kurtosis-1.2
Mean1470.5
Median Absolute Deviation (MAD)735
Skewness0
Sum4323270
Variance720545
MonotonicityStrictly increasing
2022-12-19T19:36:13.418267image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
< 0.1%
19541
 
< 0.1%
19561
 
< 0.1%
19571
 
< 0.1%
19581
 
< 0.1%
19591
 
< 0.1%
19601
 
< 0.1%
19611
 
< 0.1%
19621
 
< 0.1%
19631
 
< 0.1%
Other values (2930)2930
99.7%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
29401
< 0.1%
29391
< 0.1%
29381
< 0.1%
29371
< 0.1%
29361
< 0.1%
29351
< 0.1%
29341
< 0.1%
29331
< 0.1%
29321
< 0.1%
29311
< 0.1%

EnvironmentSatisfaction
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
3
906 
4
892 
2
574 
1
568 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2940
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row4
4th row4
5th row1

Common Values

ValueCountFrequency (%)
3906
30.8%
4892
30.3%
2574
19.5%
1568
19.3%

Length

2022-12-19T19:36:13.555640image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-19T19:36:13.699261image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
3906
30.8%
4892
30.3%
2574
19.5%
1568
19.3%

Most occurring characters

ValueCountFrequency (%)
3906
30.8%
4892
30.3%
2574
19.5%
1568
19.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2940
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3906
30.8%
4892
30.3%
2574
19.5%
1568
19.3%

Most occurring scripts

ValueCountFrequency (%)
Common2940
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3906
30.8%
4892
30.3%
2574
19.5%
1568
19.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2940
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3906
30.8%
4892
30.3%
2574
19.5%
1568
19.3%

Gender
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
Male
1764 
Female
1176 

Length

Max length6
Median length4
Mean length4.8
Min length4

Characters and Unicode

Total characters14112
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowMale
4th rowFemale
5th rowMale

Common Values

ValueCountFrequency (%)
Male1764
60.0%
Female1176
40.0%

Length

2022-12-19T19:36:13.859214image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-19T19:36:14.010513image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
male1764
60.0%
female1176
40.0%

Most occurring characters

ValueCountFrequency (%)
e4116
29.2%
a2940
20.8%
l2940
20.8%
M1764
12.5%
F1176
 
8.3%
m1176
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter11172
79.2%
Uppercase Letter2940
 
20.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e4116
36.8%
a2940
26.3%
l2940
26.3%
m1176
 
10.5%
Uppercase Letter
ValueCountFrequency (%)
M1764
60.0%
F1176
40.0%

Most occurring scripts

ValueCountFrequency (%)
Latin14112
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e4116
29.2%
a2940
20.8%
l2940
20.8%
M1764
12.5%
F1176
 
8.3%
m1176
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII14112
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e4116
29.2%
a2940
20.8%
l2940
20.8%
M1764
12.5%
F1176
 
8.3%
m1176
 
8.3%

HourlyRate
Real number (ℝ≥0)

Distinct71
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.89115646
Minimum30
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2022-12-19T19:36:14.149618image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile33
Q148
median66
Q384
95-th percentile97
Maximum100
Range70
Interquartile range (IQR)36

Descriptive statistics

Standard deviation20.32596874
Coefficient of variation (CV)0.308477948
Kurtosis-1.196405417
Mean65.89115646
Median Absolute Deviation (MAD)18
Skewness-0.03229445229
Sum193720
Variance413.1450051
MonotonicityNot monotonic
2022-12-19T19:36:14.321630image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6658
 
2.0%
9856
 
1.9%
4256
 
1.9%
4856
 
1.9%
8456
 
1.9%
5754
 
1.8%
7954
 
1.8%
9654
 
1.8%
5452
 
1.8%
5252
 
1.8%
Other values (61)2392
81.4%
ValueCountFrequency (%)
3038
1.3%
3130
1.0%
3248
1.6%
3338
1.3%
3424
0.8%
3536
1.2%
3636
1.2%
3736
1.2%
3826
0.9%
3934
1.2%
ValueCountFrequency (%)
10038
1.3%
9940
1.4%
9856
1.9%
9742
1.4%
9654
1.8%
9546
1.6%
9444
1.5%
9332
1.1%
9250
1.7%
9136
1.2%

JobInvolvement
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
3
1736 
2
750 
4
288 
1
 
166

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2940
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
31736
59.0%
2750
25.5%
4288
 
9.8%
1166
 
5.6%

Length

2022-12-19T19:36:14.479598image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-19T19:36:14.622892image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
31736
59.0%
2750
25.5%
4288
 
9.8%
1166
 
5.6%

Most occurring characters

ValueCountFrequency (%)
31736
59.0%
2750
25.5%
4288
 
9.8%
1166
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2940
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
31736
59.0%
2750
25.5%
4288
 
9.8%
1166
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common2940
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
31736
59.0%
2750
25.5%
4288
 
9.8%
1166
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII2940
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
31736
59.0%
2750
25.5%
4288
 
9.8%
1166
 
5.6%

JobLevel
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
1
1086 
2
1068 
3
436 
4
212 
5
138 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2940
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
11086
36.9%
21068
36.3%
3436
14.8%
4212
 
7.2%
5138
 
4.7%

Length

2022-12-19T19:36:14.723731image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-19T19:36:14.891623image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
11086
36.9%
21068
36.3%
3436
14.8%
4212
 
7.2%
5138
 
4.7%

Most occurring characters

ValueCountFrequency (%)
11086
36.9%
21068
36.3%
3436
14.8%
4212
 
7.2%
5138
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2940
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
11086
36.9%
21068
36.3%
3436
14.8%
4212
 
7.2%
5138
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Common2940
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
11086
36.9%
21068
36.3%
3436
14.8%
4212
 
7.2%
5138
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII2940
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11086
36.9%
21068
36.3%
3436
14.8%
4212
 
7.2%
5138
 
4.7%

JobRole
Categorical

HIGH CORRELATION

Distinct9
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
Sales Executive
652 
Research Scientist
584 
Laboratory Technician
518 
Manufacturing Director
290 
Healthcare Representative
262 
Other values (4)
634 

Length

Max length25
Median length21
Mean length18.0707483
Min length7

Characters and Unicode

Total characters53128
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSales Executive
2nd rowResearch Scientist
3rd rowLaboratory Technician
4th rowResearch Scientist
5th rowLaboratory Technician

Common Values

ValueCountFrequency (%)
Sales Executive652
22.2%
Research Scientist584
19.9%
Laboratory Technician518
17.6%
Manufacturing Director290
9.9%
Healthcare Representative262
8.9%
Manager204
 
6.9%
Sales Representative166
 
5.6%
Research Director160
 
5.4%
Human Resources104
 
3.5%

Length

2022-12-19T19:36:15.032288image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-19T19:36:15.213384image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
sales818
14.4%
research744
13.1%
executive652
11.5%
scientist584
10.3%
laboratory518
9.1%
technician518
9.1%
director450
7.9%
representative428
7.5%
manufacturing290
 
5.1%
healthcare262
 
4.6%
Other values (3)412
7.3%

Most occurring characters

ValueCountFrequency (%)
e7810
14.7%
a5160
 
9.7%
t4196
 
7.9%
c4122
 
7.8%
i4024
 
7.6%
r3968
 
7.5%
n2936
 
5.5%
s2782
 
5.2%
2736
 
5.1%
o1590
 
3.0%
Other values (19)13804
26.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter44716
84.2%
Uppercase Letter5676
 
10.7%
Space Separator2736
 
5.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e7810
17.5%
a5160
11.5%
t4196
9.4%
c4122
9.2%
i4024
9.0%
r3968
8.9%
n2936
 
6.6%
s2782
 
6.2%
o1590
 
3.6%
h1524
 
3.4%
Other values (10)6604
14.8%
Uppercase Letter
ValueCountFrequency (%)
S1402
24.7%
R1276
22.5%
E652
11.5%
L518
 
9.1%
T518
 
9.1%
M494
 
8.7%
D450
 
7.9%
H366
 
6.4%
Space Separator
ValueCountFrequency (%)
2736
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin50392
94.9%
Common2736
 
5.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e7810
15.5%
a5160
10.2%
t4196
 
8.3%
c4122
 
8.2%
i4024
 
8.0%
r3968
 
7.9%
n2936
 
5.8%
s2782
 
5.5%
o1590
 
3.2%
h1524
 
3.0%
Other values (18)12280
24.4%
Common
ValueCountFrequency (%)
2736
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII53128
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e7810
14.7%
a5160
 
9.7%
t4196
 
7.9%
c4122
 
7.8%
i4024
 
7.6%
r3968
 
7.5%
n2936
 
5.5%
s2782
 
5.2%
2736
 
5.1%
o1590
 
3.0%
Other values (19)13804
26.0%

JobSatisfaction
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
4
918 
3
884 
1
578 
2
560 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2940
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row2
3rd row3
4th row3
5th row2

Common Values

ValueCountFrequency (%)
4918
31.2%
3884
30.1%
1578
19.7%
2560
19.0%

Length

2022-12-19T19:36:15.381284image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-19T19:36:15.524866image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
4918
31.2%
3884
30.1%
1578
19.7%
2560
19.0%

Most occurring characters

ValueCountFrequency (%)
4918
31.2%
3884
30.1%
1578
19.7%
2560
19.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2940
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4918
31.2%
3884
30.1%
1578
19.7%
2560
19.0%

Most occurring scripts

ValueCountFrequency (%)
Common2940
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4918
31.2%
3884
30.1%
1578
19.7%
2560
19.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2940
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4918
31.2%
3884
30.1%
1578
19.7%
2560
19.0%

MaritalStatus
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
Married
1346 
Single
940 
Divorced
654 

Length

Max length8
Median length7
Mean length6.902721088
Min length6

Characters and Unicode

Total characters20294
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle
2nd rowMarried
3rd rowSingle
4th rowMarried
5th rowMarried

Common Values

ValueCountFrequency (%)
Married1346
45.8%
Single940
32.0%
Divorced654
22.2%

Length

2022-12-19T19:36:15.653647image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-19T19:36:15.796254image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
married1346
45.8%
single940
32.0%
divorced654
22.2%

Most occurring characters

ValueCountFrequency (%)
r3346
16.5%
i2940
14.5%
e2940
14.5%
d2000
9.9%
M1346
6.6%
a1346
6.6%
S940
 
4.6%
n940
 
4.6%
g940
 
4.6%
l940
 
4.6%
Other values (4)2616
12.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter17354
85.5%
Uppercase Letter2940
 
14.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r3346
19.3%
i2940
16.9%
e2940
16.9%
d2000
11.5%
a1346
7.8%
n940
 
5.4%
g940
 
5.4%
l940
 
5.4%
v654
 
3.8%
o654
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
M1346
45.8%
S940
32.0%
D654
22.2%

Most occurring scripts

ValueCountFrequency (%)
Latin20294
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r3346
16.5%
i2940
14.5%
e2940
14.5%
d2000
9.9%
M1346
6.6%
a1346
6.6%
S940
 
4.6%
n940
 
4.6%
g940
 
4.6%
l940
 
4.6%
Other values (4)2616
12.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII20294
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r3346
16.5%
i2940
14.5%
e2940
14.5%
d2000
9.9%
M1346
6.6%
a1346
6.6%
S940
 
4.6%
n940
 
4.6%
g940
 
4.6%
l940
 
4.6%
Other values (4)2616
12.9%

MonthlyIncome
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1349
Distinct (%)45.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6502.931293
Minimum1009
Maximum19999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2022-12-19T19:36:15.921673image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1009
5-th percentile2097
Q12911
median4919
Q38380
95-th percentile17856
Maximum19999
Range18990
Interquartile range (IQR)5469

Descriptive statistics

Standard deviation4707.15577
Coefficient of variation (CV)0.7238513768
Kurtosis1.001480439
Mean6502.931293
Median Absolute Deviation (MAD)2199
Skewness1.369117141
Sum19118618
Variance22157315.44
MonotonicityNot monotonic
2022-12-19T19:36:16.096565image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23428
 
0.3%
61426
 
0.2%
27416
 
0.2%
25596
 
0.2%
26106
 
0.2%
24516
 
0.2%
55626
 
0.2%
34526
 
0.2%
23806
 
0.2%
63476
 
0.2%
Other values (1339)2878
97.9%
ValueCountFrequency (%)
10092
0.1%
10512
0.1%
10522
0.1%
10812
0.1%
10912
0.1%
11022
0.1%
11182
0.1%
11292
0.1%
12002
0.1%
12232
0.1%
ValueCountFrequency (%)
199992
0.1%
199732
0.1%
199432
0.1%
199262
0.1%
198592
0.1%
198472
0.1%
198452
0.1%
198332
0.1%
197402
0.1%
197172
0.1%

MonthlyRate
Real number (ℝ≥0)

Distinct1427
Distinct (%)48.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14313.1034
Minimum2094
Maximum26999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2022-12-19T19:36:16.290164image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum2094
5-th percentile3376
Q18045
median14235.5
Q320462
95-th percentile25440
Maximum26999
Range24905
Interquartile range (IQR)12417

Descriptive statistics

Standard deviation7116.575021
Coefficient of variation (CV)0.4972069873
Kurtosis-1.214931492
Mean14313.1034
Median Absolute Deviation (MAD)6206.5
Skewness0.01856832054
Sum42080524
Variance50645640.03
MonotonicityNot monotonic
2022-12-19T19:36:16.454783image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42236
 
0.2%
91506
 
0.2%
95584
 
0.1%
128584
 
0.1%
220744
 
0.1%
253264
 
0.1%
90964
 
0.1%
130084
 
0.1%
123554
 
0.1%
77444
 
0.1%
Other values (1417)2896
98.5%
ValueCountFrequency (%)
20942
0.1%
20972
0.1%
21042
0.1%
21122
0.1%
21222
0.1%
21254
0.1%
21372
0.1%
22272
0.1%
22432
0.1%
22532
0.1%
ValueCountFrequency (%)
269992
0.1%
269972
0.1%
269682
0.1%
269592
0.1%
269562
0.1%
269332
0.1%
269142
0.1%
268972
0.1%
268942
0.1%
268622
0.1%

NumCompaniesWorked
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.693197279
Minimum0
Maximum9
Zeros394
Zeros (%)13.4%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2022-12-19T19:36:16.596988image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.497583994
Coefficient of variation (CV)0.927367636
Kurtosis0.008154234519
Mean2.693197279
Median Absolute Deviation (MAD)1
Skewness1.025946912
Sum7918
Variance6.237925807
MonotonicityNot monotonic
2022-12-19T19:36:16.694989image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
11042
35.4%
0394
 
13.4%
3318
 
10.8%
2292
 
9.9%
4278
 
9.5%
7148
 
5.0%
6140
 
4.8%
5126
 
4.3%
9104
 
3.5%
898
 
3.3%
ValueCountFrequency (%)
0394
 
13.4%
11042
35.4%
2292
 
9.9%
3318
 
10.8%
4278
 
9.5%
5126
 
4.3%
6140
 
4.8%
7148
 
5.0%
898
 
3.3%
9104
 
3.5%
ValueCountFrequency (%)
9104
 
3.5%
898
 
3.3%
7148
 
5.0%
6140
 
4.8%
5126
 
4.3%
4278
 
9.5%
3318
 
10.8%
2292
 
9.9%
11042
35.4%
0394
 
13.4%

Over18
Boolean

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
True
2940 
ValueCountFrequency (%)
True2940
100.0%
2022-12-19T19:36:16.824545image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

OverTime
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
False
2108 
True
832 
ValueCountFrequency (%)
False2108
71.7%
True832
 
28.3%
2022-12-19T19:36:16.929118image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

PercentSalaryHike
Real number (ℝ≥0)

HIGH CORRELATION

Distinct15
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.20952381
Minimum11
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2022-12-19T19:36:17.023479image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q112
median14
Q318
95-th percentile22
Maximum25
Range14
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.659315013
Coefficient of variation (CV)0.2405936609
Kurtosis-0.3021290683
Mean15.20952381
Median Absolute Deviation (MAD)2
Skewness0.8207086405
Sum44716
Variance13.39058637
MonotonicityNot monotonic
2022-12-19T19:36:17.150398image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
11420
14.3%
13418
14.2%
14402
13.7%
12396
13.5%
15202
6.9%
18178
6.1%
17164
 
5.6%
16156
 
5.3%
19152
 
5.2%
22112
 
3.8%
Other values (5)340
11.6%
ValueCountFrequency (%)
11420
14.3%
12396
13.5%
13418
14.2%
14402
13.7%
15202
6.9%
16156
 
5.3%
17164
 
5.6%
18178
6.1%
19152
 
5.2%
20110
 
3.7%
ValueCountFrequency (%)
2536
 
1.2%
2442
 
1.4%
2356
 
1.9%
22112
3.8%
2196
3.3%
20110
3.7%
19152
5.2%
18178
6.1%
17164
5.6%
16156
5.3%

PerformanceRating
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
3
2488 
4
452 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2940
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row4
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
32488
84.6%
4452
 
15.4%

Length

2022-12-19T19:36:17.276052image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-19T19:36:17.406791image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
32488
84.6%
4452
 
15.4%

Most occurring characters

ValueCountFrequency (%)
32488
84.6%
4452
 
15.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2940
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
32488
84.6%
4452
 
15.4%

Most occurring scripts

ValueCountFrequency (%)
Common2940
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
32488
84.6%
4452
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII2940
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
32488
84.6%
4452
 
15.4%

RelationshipSatisfaction
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
3
918 
4
864 
2
606 
1
552 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2940
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row4
3rd row2
4th row3
5th row4

Common Values

ValueCountFrequency (%)
3918
31.2%
4864
29.4%
2606
20.6%
1552
18.8%

Length

2022-12-19T19:36:17.511797image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-19T19:36:17.645638image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
3918
31.2%
4864
29.4%
2606
20.6%
1552
18.8%

Most occurring characters

ValueCountFrequency (%)
3918
31.2%
4864
29.4%
2606
20.6%
1552
18.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2940
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3918
31.2%
4864
29.4%
2606
20.6%
1552
18.8%

Most occurring scripts

ValueCountFrequency (%)
Common2940
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3918
31.2%
4864
29.4%
2606
20.6%
1552
18.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII2940
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3918
31.2%
4864
29.4%
2606
20.6%
1552
18.8%

StandardHours
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
80
2940 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters5880
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row80
2nd row80
3rd row80
4th row80
5th row80

Common Values

ValueCountFrequency (%)
802940
100.0%

Length

2022-12-19T19:36:17.778885image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-19T19:36:17.901755image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
802940
100.0%

Most occurring characters

ValueCountFrequency (%)
82940
50.0%
02940
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5880
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
82940
50.0%
02940
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common5880
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
82940
50.0%
02940
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5880
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
82940
50.0%
02940
50.0%

StockOptionLevel
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
0
1262 
1
1192 
2
316 
3
170 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2940
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
01262
42.9%
11192
40.5%
2316
 
10.7%
3170
 
5.8%

Length

2022-12-19T19:36:17.994763image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-19T19:36:18.111130image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
01262
42.9%
11192
40.5%
2316
 
10.7%
3170
 
5.8%

Most occurring characters

ValueCountFrequency (%)
01262
42.9%
11192
40.5%
2316
 
10.7%
3170
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2940
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01262
42.9%
11192
40.5%
2316
 
10.7%
3170
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
Common2940
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01262
42.9%
11192
40.5%
2316
 
10.7%
3170
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII2940
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01262
42.9%
11192
40.5%
2316
 
10.7%
3170
 
5.8%

TotalWorkingYears
Real number (ℝ≥0)

HIGH CORRELATION

Distinct40
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.27959184
Minimum0
Maximum40
Zeros22
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2022-12-19T19:36:18.254485image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median10
Q315
95-th percentile28
Maximum40
Range40
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.77945785
Coefficient of variation (CV)0.6896932055
Kurtosis0.9146652211
Mean11.27959184
Median Absolute Deviation (MAD)4
Skewness1.116601334
Sum33162
Variance60.51996445
MonotonicityNot monotonic
2022-12-19T19:36:18.418329image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
10404
 
13.7%
6250
 
8.5%
8206
 
7.0%
9192
 
6.5%
5176
 
6.0%
7162
 
5.5%
1162
 
5.5%
4126
 
4.3%
1296
 
3.3%
384
 
2.9%
Other values (30)1082
36.8%
ValueCountFrequency (%)
022
 
0.7%
1162
5.5%
262
 
2.1%
384
 
2.9%
4126
4.3%
5176
6.0%
6250
8.5%
7162
5.5%
8206
7.0%
9192
6.5%
ValueCountFrequency (%)
404
 
0.1%
382
 
0.1%
378
0.3%
3612
0.4%
356
 
0.2%
3410
0.3%
3314
0.5%
3218
0.6%
3118
0.6%
3014
0.5%

TrainingTimesLastYear
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.799319728
Minimum0
Maximum6
Zeros108
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2022-12-19T19:36:18.556861image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.289051264
Coefficient of variation (CV)0.4604873288
Kurtosis0.4921087248
Mean2.799319728
Median Absolute Deviation (MAD)1
Skewness0.5528417007
Sum8230
Variance1.661653161
MonotonicityNot monotonic
2022-12-19T19:36:18.661319image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
21094
37.2%
3982
33.4%
4246
 
8.4%
5238
 
8.1%
1142
 
4.8%
6130
 
4.4%
0108
 
3.7%
ValueCountFrequency (%)
0108
 
3.7%
1142
 
4.8%
21094
37.2%
3982
33.4%
4246
 
8.4%
5238
 
8.1%
6130
 
4.4%
ValueCountFrequency (%)
6130
 
4.4%
5238
 
8.1%
4246
 
8.4%
3982
33.4%
21094
37.2%
1142
 
4.8%
0108
 
3.7%

WorkLifeBalance
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
3
1786 
2
688 
4
306 
1
 
160

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2940
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
31786
60.7%
2688
 
23.4%
4306
 
10.4%
1160
 
5.4%

Length

2022-12-19T19:36:18.770724image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-19T19:36:18.922287image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
31786
60.7%
2688
 
23.4%
4306
 
10.4%
1160
 
5.4%

Most occurring characters

ValueCountFrequency (%)
31786
60.7%
2688
 
23.4%
4306
 
10.4%
1160
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2940
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
31786
60.7%
2688
 
23.4%
4306
 
10.4%
1160
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Common2940
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
31786
60.7%
2688
 
23.4%
4306
 
10.4%
1160
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII2940
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
31786
60.7%
2688
 
23.4%
4306
 
10.4%
1160
 
5.4%

YearsAtCompany
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct37
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.008163265
Minimum0
Maximum40
Zeros88
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2022-12-19T19:36:19.037122image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q39
95-th percentile20
Maximum40
Range40
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.125482783
Coefficient of variation (CV)0.8740496691
Kurtosis3.926771682
Mean7.008163265
Median Absolute Deviation (MAD)3
Skewness1.763628341
Sum20604
Variance37.52153933
MonotonicityNot monotonic
2022-12-19T19:36:19.194906image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
5392
13.3%
1342
11.6%
3256
8.7%
2254
8.6%
10240
8.2%
4220
 
7.5%
7180
 
6.1%
9164
 
5.6%
8160
 
5.4%
6152
 
5.2%
Other values (27)580
19.7%
ValueCountFrequency (%)
088
 
3.0%
1342
11.6%
2254
8.6%
3256
8.7%
4220
7.5%
5392
13.3%
6152
 
5.2%
7180
6.1%
8160
5.4%
9164
5.6%
ValueCountFrequency (%)
402
 
0.1%
372
 
0.1%
364
 
0.1%
342
 
0.1%
3310
0.3%
326
0.2%
316
0.2%
302
 
0.1%
294
 
0.1%
274
 
0.1%

YearsInCurrentRole
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct19
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.229251701
Minimum0
Maximum18
Zeros488
Zeros (%)16.6%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2022-12-19T19:36:19.343917image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile11
Maximum18
Range18
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.622520593
Coefficient of variation (CV)0.8565393713
Kurtosis0.4745664051
Mean4.229251701
Median Absolute Deviation (MAD)3
Skewness0.9168946757
Sum12434
Variance13.12265545
MonotonicityNot monotonic
2022-12-19T19:36:19.455729image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2744
25.3%
0488
16.6%
7444
15.1%
3270
 
9.2%
4208
 
7.1%
8178
 
6.1%
9134
 
4.6%
1114
 
3.9%
674
 
2.5%
572
 
2.4%
Other values (9)214
 
7.3%
ValueCountFrequency (%)
0488
16.6%
1114
 
3.9%
2744
25.3%
3270
 
9.2%
4208
 
7.1%
572
 
2.4%
674
 
2.5%
7444
15.1%
8178
 
6.1%
9134
 
4.6%
ValueCountFrequency (%)
184
 
0.1%
178
 
0.3%
1614
 
0.5%
1516
 
0.5%
1422
 
0.7%
1328
 
1.0%
1220
 
0.7%
1144
 
1.5%
1058
2.0%
9134
4.6%

YearsSinceLastPromotion
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct16
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.187755102
Minimum0
Maximum15
Zeros1162
Zeros (%)39.5%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2022-12-19T19:36:19.596489image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile9
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.221882014
Coefficient of variation (CV)1.472688607
Kurtosis3.604485231
Mean2.187755102
Median Absolute Deviation (MAD)1
Skewness1.983276643
Sum6432
Variance10.38052371
MonotonicityNot monotonic
2022-12-19T19:36:19.720684image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
01162
39.5%
1714
24.3%
2318
 
10.8%
7152
 
5.2%
4122
 
4.1%
3104
 
3.5%
590
 
3.1%
664
 
2.2%
1148
 
1.6%
836
 
1.2%
Other values (6)130
 
4.4%
ValueCountFrequency (%)
01162
39.5%
1714
24.3%
2318
 
10.8%
3104
 
3.5%
4122
 
4.1%
590
 
3.1%
664
 
2.2%
7152
 
5.2%
836
 
1.2%
934
 
1.2%
ValueCountFrequency (%)
1526
 
0.9%
1418
 
0.6%
1320
 
0.7%
1220
 
0.7%
1148
 
1.6%
1012
 
0.4%
934
 
1.2%
836
 
1.2%
7152
5.2%
664
2.2%

YearsWithCurrManager
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct18
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.123129252
Minimum0
Maximum17
Zeros526
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2022-12-19T19:36:19.842406image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile10
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.567529037
Coefficient of variation (CV)0.8652479267
Kurtosis0.1687248825
Mean4.123129252
Median Absolute Deviation (MAD)3
Skewness0.8330253638
Sum12122
Variance12.72726343
MonotonicityNot monotonic
2022-12-19T19:36:19.967446image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2688
23.4%
0526
17.9%
7432
14.7%
3284
9.7%
8214
 
7.3%
4196
 
6.7%
1152
 
5.2%
9128
 
4.4%
562
 
2.1%
658
 
2.0%
Other values (8)200
 
6.8%
ValueCountFrequency (%)
0526
17.9%
1152
 
5.2%
2688
23.4%
3284
9.7%
4196
 
6.7%
562
 
2.1%
658
 
2.0%
7432
14.7%
8214
 
7.3%
9128
 
4.4%
ValueCountFrequency (%)
1714
 
0.5%
164
 
0.1%
1510
 
0.3%
1410
 
0.3%
1328
 
1.0%
1236
 
1.2%
1144
 
1.5%
1054
 
1.8%
9128
4.4%
8214
7.3%

Interactions

2022-12-19T19:36:06.815326image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-19T19:35:35.786900image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-19T19:35:37.798821image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-19T19:35:40.089435image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-19T19:35:42.226590image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-19T19:35:44.556166image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-19T19:35:46.715963image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-19T19:35:48.942610image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-19T19:35:51.262931image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-19T19:35:53.340874image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-19T19:35:55.472088image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-19T19:35:57.690425image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-19T19:36:00.172333image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-19T19:36:02.405411image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-19T19:36:04.618922image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-19T19:36:06.955373image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-19T19:35:35.918722image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-19T19:35:37.926965image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2022-12-19T19:35:36.180439image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-19T19:35:38.349538image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-19T19:35:40.495679image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-19T19:35:42.641865image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-19T19:35:44.974946image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-19T19:35:47.138548image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-19T19:35:49.355387image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-19T19:35:51.663739image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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Correlations

2022-12-19T19:36:20.155003image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-19T19:36:20.600616image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-19T19:36:21.429708image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-19T19:36:21.801601image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-12-19T19:36:22.118251image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-19T19:36:09.604308image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-19T19:36:10.404254image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

AgeAttritionBusinessTravelDailyRateDepartmentDistanceFromHomeEducationEducationFieldEmployeeCountEmployeeNumberEnvironmentSatisfactionGenderHourlyRateJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusMonthlyIncomeMonthlyRateNumCompaniesWorkedOver18OverTimePercentSalaryHikePerformanceRatingRelationshipSatisfactionStandardHoursStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManager
0411Travel_Rarely1102Sales12Life Sciences112Female9432Sales Executive4Single5993194798YYes11318008016405
1490Travel_Frequently279Research & Development81Life Sciences123Male6122Research Scientist2Married5130249071YNo2344801103310717
2371Travel_Rarely1373Research & Development22Other134Male9221Laboratory Technician3Single209023966YYes15328007330000
3330Travel_Frequently1392Research & Development34Life Sciences144Female5631Research Scientist3Married2909231591YYes11338008338730
4270Travel_Rarely591Research & Development21Medical151Male4031Laboratory Technician2Married3468166329YNo12348016332222
5320Travel_Frequently1005Research & Development22Life Sciences164Male7931Laboratory Technician4Single3068118640YNo13338008227736
6590Travel_Rarely1324Research & Development33Medical173Female8141Laboratory Technician1Married267099644YYes204180312321000
7300Travel_Rarely1358Research & Development241Life Sciences184Male6731Laboratory Technician3Divorced2693133351YNo22428011231000
8380Travel_Frequently216Research & Development233Life Sciences194Male4423Manufacturing Director3Single952687870YNo214280010239718
9360Travel_Rarely1299Research & Development273Medical1103Male9432Healthcare Representative3Married5237165776YNo133280217327777

Last rows

AgeAttritionBusinessTravelDailyRateDepartmentDistanceFromHomeEducationEducationFieldEmployeeCountEmployeeNumberEnvironmentSatisfactionGenderHourlyRateJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusMonthlyIncomeMonthlyRateNumCompaniesWorkedOver18OverTimePercentSalaryHikePerformanceRatingRelationshipSatisfactionStandardHoursStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManager
2930290Travel_Rarely468Research & Development284Medical129314Female7321Research Scientist1Single378584891YNo14328005315404
2931501Travel_Rarely410Sales283Marketing129324Male3923Sales Executive1Divorced10854165864YYes133280120333220
2932390Travel_Rarely722Sales241Marketing129332Female6024Sales Executive4Married1203188280YNo1131801212220996
2933310Non-Travel325Research & Development53Medical129342Male7432Manufacturing Director1Single993637870YNo193280010239417
2934260Travel_Rarely1167Sales53Other129354Female3021Sales Representative3Single2966213780YNo18348005234200
2935360Travel_Frequently884Research & Development232Medical129363Male4142Laboratory Technician4Married2571122904YNo173380117335203
2936390Travel_Rarely613Research & Development61Medical129374Male4223Healthcare Representative1Married9991214574YNo15318019537717
2937270Travel_Rarely155Research & Development43Life Sciences129382Male8742Manufacturing Director2Married614251741YYes20428016036203
2938490Travel_Frequently1023Sales23Medical129394Male6322Sales Executive2Married5390132432YNo143480017329608
2939340Travel_Rarely628Research & Development83Medical129402Male8242Laboratory Technician3Married4404102282YNo12318006344312